Slides presenting the findings of my Research Project for my Masters in Applied Economics

1. Does FDI increases GDP per capita
growth in the Former Soviet Union?
By Roger Miller
I. Introduction:
The Soviet Union was a centrally planned economy that is unparalleled in history. It set
up heavy industrial sectors without enough light industry and overpaid agriculture. When
compared to structures like China’s economy before the year 1978 at which time China started
to move more toward a market economy, the Soviet Union left a much more difficult problem
for restructuring (Sachs and Woo 1994). The Soviet Union had greater citizen benefits all the
way around, but largely connected them to the place of employment. Changing the market
would mean more people would be changing jobs, some firms would be forced out of business
or to lay people off. That would mean those people would lose their benefits. Fear of such
changes has likely been a driving factor of the reluctance of the citizens to make the change. It
is likely that they feel like the gains they will eventually get from moving to a free market
economy are just too far in the future for the turmoil they will have to go through to get there.
Although their history is unequalled in magnitude of a socially planned economy, its
future possibility of market economic structure exists in other countries. Many great nations
benefit from market economies and the specialization and profit maximizing efficiency inducing
practices that go with them. That means that the Former Soviet Union (FSU) can look to other
countries as examples for the market both on macro and micro aspects. Governments can
learn from policies of other governments and individual firms can gain from Foreign Direct

2. Investment (FDI). The goal of this paper is to show the positive benefits the FSU is gaining in
their economic growth as a result of FDI if any may be determined or supported empirically.
Generally FDI is expected to increase growth both directly and indirectly. FDI is believed
to have positive effects such as productivity gains, technology transfers, exposure to new
processes, managerial skills, employee training, international production networks, and access
to markets (Alfaro et al. 2007). Firm level studies have not shown positive effects on economic
growth including technology spillovers (Carkovic and Levine 2002). Such benefits are difficult to
measure directly. Using GDP growth rates and FDI levels, I will attempt to determine whether
FDI has a greater effect in the FSU than in other nations.
Macroeconomic studies have shown that FDI is good for growth in an economy with
financial markets that can manage the flows, but it is argued that these analyses are not careful
enough in their calculations for things like simultaneity and country-specific effects (Carkovic
and Levine 2002). The restructuring of the economy to where the public is now responsible for
finding their niche in the marketplace, gives rise to grow to its natural market potential.
Convergence will bring the less developed nations up to speed with the other markets. If
nations with slower Gross Domestic Product (GDP) do catch up, then we should see the FSU
growing quickly (de la Fuente 1997).
A study over the period from 1994 to 1998 found that FDI in Europe’s formerly centrally
planned economies, have many determining factors to inflows of FDI including country risk, unit
labor cost, host market size and gravity factors. It also found that the announcing of a country
preparing to join the European Union (EU) caused increased faith in the economy of that
country and FDI inflows increased as a result which further improved the performance of those
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3. emerging economies. Conversely countries that were not members of the EU, and did not have
plans to join would not receive as much FDI and would continue to struggle for stability and
growth progress (Bevan and Estrin 2000). Such findings support the theory that FDI can help
economies to improve their performance. The intent here is to show that it applies at a larger
level to the FSU than average.
It is also shown that multinational corporations (MNC’s) are more technologically
advanced than average, and as such are able to bring new technology into the countries in
which they invest. This follows the theory of FDI increasing production through technology
spillovers. The marginal cost of these MNC’s is lower for developing and replicating new
technologies for their production. A large limiting factor in underdeveloped nations is human
capital (Borensztein et al. 1998). The work force must be able to learn the new jobs without
excessive training in order for the corporations and economies to benefit from it.
In this paper, I aim to show the positive aspects of FDI and what the FSU nations can
hope to gain from continuing to open their borders to investment. There are other papers that
deal more with the effects of the structure set up in industry under communism and the
difficulties that come with restructuring to a market economy that no longer has to follow such
mandates as from a social planner. For further reading on that, I would recommend “Corporate
Governance in the Former Soviet Union: An Overview” by Saul Estrin and Mike Wright 1999.
I am isolating the FSU from the rest of the nations because of the history of them being
combined into the same government and economic society, and then splitting into separate
governments and economies. Any corruption or political boundaries that may come into effect
are difficult to measure and collect data on. Inferences may be made, but the issue is a difficult
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4. one to resolve. Some analysis with such indicators will be attempted in this paper, but the
findings are not robust enough to be strong indicators.
I believe that while such things as governance and corruption may cause problems, it
may not be much worse than with most countries and is subjective in measurement, so that the
bulk of the benefit and difficulty with these economies comes more from their being set up by a
social planner in a less than optimal fashion, or at least one that a market structure would
support, and their ability to restructure to achieve desired economic growth to increase welfare
and utility of the general public.
Investigating the impact of FDI in the FSU has potential to aid in improving our
understanding of how to help transitioning economies. Such benefits can be from showing
corporations how to more securely enter the restructuring markets. FDI helps growth where
financial markets are in good condition which shows transitioning economies to first strengthen
their financial sector and to improve trade restrictions that promote FDI. As mentioned earlier,
it is also important to have human capital at a level comparable to the FDI it wishes to attract.
The rest of the paper is divided into five sections. The next section discusses the model
and its theory. Section three presents the variables for analysis, while section four presents the
data. Section five discusses the regression results. Section six is the conclusion.
II. Structure of the Model:
The form of the model presented here is Ordinary Least Squares (OLS).
This gives a basic model that makes the affect clearer since it is the coefficient of FDI in FSU
nations. The growth rate of Gross Domestic Product is the most widely used indicators of
economic growth and development. Therefore it is used here as the dependent variable
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5. explained by a series of independent variables. It is expected that FDI in the FSU will have a
positive effect since the socialist structure of their economies is not as efficient for producing
technology. It is expected that the FSU will best benefit from FDI with restructuring their
economies with more profitable organization, especially in management. The structure of the
model is:
GDPgrowth  1 * FDI * FSU  2 * FDI  3 * FSU  controls
In this model I start in 1995 to allow for some of the stress of the transition to be
relieved so that economies showing promise and stabilizing are more likely to attract FDI
inflows that will be of more significance to them in their growth efforts. The data is therefore
more limited, but gives results that are a better representation of the progress the nations are
making as they become more capitalistic and is a better representation of the new growth
these transitional economies are now experiencing. It is hoped that the data is more consistent
and therefore more reliable in analysis.
I created a variable to represent FDI in just the FSU nations, which is an indicator
variable multiplied by the FDI inflows into all nations. It therefore only has values for the 12
FSU nations consistent with their FDI inflows, and is zero for all other nations.
The other variables in this analysis include Gross Fixed Capital Formation (as a
percentage of GDP) representing the industrialization that accompanies and promotes growth,
Secondary School Enrollment (as a percentage of gross student age population) to represent
human capital, Crude Birth Rate (per 1000 people) as another measure of human capital, the
natural logarithm of GDP in 1999 as a measure of initial capital stock, openness to trade,
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6. inflation, and Gross Domestic Savings (as a percentage of GDP).
As noted in the introduction, when a country develops an encouraging growth trend, it
attracts more FDI. There are some concerns of endogeneity and causality in the model for such
reasons. FDI may increase because growth increases, growth may increase because FDI
increases, both could benefit each other, or there may be no direct effect from one to the
other. In an effort to avoid such causality issues, I used data for the independent variables from
a period preceding the data from the growth or dependant variable. This keeps causality clear
in that the data are not taken from the same year, but are presented as the independent
variables causing the dependent variable at a later date which does allow for causality only in
one direction.
III. Variables for analysis:
The main variables of interest are Foreign Direct Investment, as well as Foreign Direct
Investment in the Former Soviet Union nations. The FDI net inflows (as a % of GDP) variable is
included for all countries, plus an additional FDI variable multiplied by an indicator of whether
the country was a member of the FSU in order to show the added benefit the FSU nations
would benefit from FDI.
Gross Domestic Savings was included in the model under the theory of it working with
FDI, instead it may be interpreted that FDI was a substitute for domestic savings. Gross Fixed
Capital Formation was included in the model in representation of industrialization which is
generally associated with economic growth.
Secondary School Enrollment rates, as a percent of the population of age for that level
of schooling, was included in the model. It is used as an indicator of human capital and labor.
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7. Crude Birth Rate was also included as a measure of human capital, it was available for much
less countries, but since education levels were higher for FSU nations there may be a negative
correlation to growth and education enrollment levels.
Openness to Trade is in the model as well, which was calculated by adding exports to
imports both of which are measured as a percent of GDP for the year 1999. Openness to trade
can be quite substantial in growth as it invites more specialization and productivity in those
areas of production in which the home country has a comparative advantage. This makes the
home country more profitable and thereby increases growth. Growth is shown to be positively
correlated to trade openness (Edwards 1997). Edwards also reminds us how complicated it can
be to determine which arena to focus our attention on when discussing and considering how to
benefit growth with trade:
“The complex nature of commercial policy – international trade can be
effected by tariffs, quotas, licenses, prohibitions, and exchange controls, among
others – suggests that attempts to construct a single indicator of trade
orientation may be futile, and will tend to generate disagreements and
controversies. (Edwards 1997)”
This issue of having so many avenues to approach in order to improve international trade, and
hopefully benefit home countries with more growth is further complicated with war getting in
the way of good trade relations especially if production facilities are damaged in battle. Even at
that, the fact that these policies and restrictions and gateways are all so inter-related makes
calculating benefits of lessening controls difficult to pinpoint so as to direct one as to where to
start. The reliability and accuracy of policy and trade indicators is certainly a difficult point to
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8. address as it makes conclusions drawn from such indicators a little more grey (Rodriguez and
Rodrik 1999).
Inflation is included under the idea that price levels affect trade and investment
between nations, and is calculated using consumer price levels from each country with the
standard as US$ in the year 2000=100, and calculating the percent change in prices from 1998
to 1999. Inflation can have varying effects on trade. As the price levels change within
countries, it alters the currency value in the exchange markets, and that causes price
differentials by virtue of the exchange rate. As the United States Dollar falls in value, American
goods become cheaper to other nations which may increase demand for American goods and
thereby increases American profitability. A large study covering data from 1960 to 1990
showed that when inflation is as much as ten percentage points per year, it can cause a drop in
the real growth rate of GDP per capita by as much as 0.3 percentage points annually, and also
reduce investment as a percent of GDP by as much as 0.6 percentage points annually (Barro
1995).
The logarithm of Initial GDP per capita was included in regressions as a measure of
initial capital. This was included in the model under the theory that it takes money to make
money, or some initial capital stock must be present in order for the economy to exist or grow.
I also ran another set of regressions with a governance indicator included to give
another term that may indicate a difference with other nations. The governance indicator I
used was developed by Kaufmann, Kraay, and Mastruzzi and a table of their indicators is
available on the World Bank Website. I used the Rule of Law indicator, and tried it in three
different ways: just with its own value, multiplied by FSU, and multiplied by FDI*FSU. There
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9. was high correlation between the five indicators they developed, so there is little difference in
the choice of variable from the set.
IV. Data:
The data was collected from the World Bank’s World Development Indicators (WDI)
2008 CD-Rom version. This was accessed through Washington State University’s subscription.
It had the variables of interest for a very large number of countries. Table one shows the
descriptive statistics of the variables in the model that were used to run regressions to test the
model (based off the Carkovic and Levine model).
Table 1: Base Year 1999 Descriptive Statistics
Standard Standard Sample
Mean Min. Max. Count
Error Deviation Variance
GDPgpc (2001-2005) 2.846 0.252 3.439 11.830 -6.000 24.057 187
FDIFSU (1996-2000) 0.257 0.083 1.078 1.163 0.000 6.597 168
FDI net inflows (1996-2000) 4.533 0.469 6.073 36.886 -3.073 51.476 168
FSU - - - - 0 1 187
Gross Domestic Savings (% of GDP) 16.079 1.088 14.306 204.649 -41.225 53.231 173
Gross Fixed Capital Formation 21.464 0.554 7.290 53.146 3.087 47.993 173
Secondary School Enrollment 67.337 2.735 33.833 1144.670 5.178 159.499 153
Crude Birth Rate 14.310 0.744 6.093 37.128 7.800 41.800 67
Log of Initial Income per Capital 7.581 0.117 1.587 2.520 4.537 10.675 185
Inflation 0.138 0.032 0.408 0.166 -0.085 2.937 162
Openness to Trade 82.943 3.248 43.459 1888.670 18.969 251.372 179
Rule of Law -0.040 0.078 1.022 1.045 -2.267 1.925 170
Rule of Law *FSU -0.035 0.017 0.215 0.046 -1.404 0.590 170
Rule of Law *FSU*FDI -0.084 0.059 0.766 0.586 -5.925 3.713 170
Data from World Bank, World Development Indicators 2008 CD-Rom version. Base year of 1999 unless
otherwise specified.
The growth of the FSU nations in constant based on the year 2000 United States dollar
value of Gross Domestic Product per Capita and is shown in table two. To show the effect of
the break up, it is important to look at the available information on their economies previous to
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10. the break up. Data for the former Soviet Union are limited, and likely not as accurate as hoped,
but still is able to give some clues as to the effects of the restructuring of their economies.
Table 2: GDP per capita (constant 2000 US$)
CountryYear 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995
Belarus 1410 1392 1256 1158 1024 920
Estonia 3873 3887 3993 4080 4190 3891 3598 2888 2794 2806 2986
Georgia 1986 1805 1809 1893 1749 1493 1188 665 479 438 458
Kazakhstan 1612 1425 1351 1235 1095 1023
Kyrgyz Republic 397 402 444 448 465 422 359 304 243 227
Latvia 3628 3777 3842 4014 4217 3901 3421 2349 2271 2356 2364
Lithuania 4337 4085 3220 2710 2462 2561
Moldova 751 803 806 813 849 824 690 489 483 334 331
Russian Federation 2693 2602 2465 2106 1926 1686 1618
Tajikistan 521 522 499 551 501 485 441 308 253 196 169
Ukraine 1408 1438 1487 1389 1270 1143 980 759 672
Uzbekistan 643 685 690 685 667 579 553 514 500
CountryYear 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
Belarus 949 1062 1156 1200 1273 1338 1412 1519 1701 1871 2067
Estonia 3164 3555 3750 3790 4106 4438 4813 5174 5610 6211 6938
Georgia 517 579 604 629 648 687 733 823 880 974 1075
Kazakhstan 1044 1078 1076 1116 1229 1397 1534 1671 1819 1978 2166
Kyrgyz Republic 240 260 261 267 279 291 289 306 324 321 326
Latvia 2477 2727 2904 3065 3302 3588 3854 4154 4539 5047 5681
Lithuania 2701 2910 3144 3112 3263 3498 3753 4158 4487 4873 5277
Moldova 316 325 307 300 311 334 365 395 430 468 492
Russian Federation 1564 1591 1511 1614 1775 1870 1968 2122 2286 2444 2620
Tajikistan 139 140 145 148 159 173 186 203 222 234 247
Ukraine 610 597 591 594 636 701 745 823 930 962 1037
Uzbekistan 499 515 528 543 558 574 590 608 647 684 724
Data from World Bank, World Development Indicators 2008 CD-Rom version.
The restructuring of economies in the case of the former Soviet Union and its breaking-
up caused an immediate downturn in their economies, after which they have regained what
they had lost in terms of GDP per capita. Estonia, Latvia, Lithuania, Kazakhstan, and Russia have
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11. actually gained more GDP per capita than they had at the break up. These countries were
showing good growth between 1985 and 1991, but when the Soviet Union split up, they fell
immediately. Russia did not start to progress again until about 1999, while the other former
Soviet Union nations began to return to positive growth paths around 1995.
Inflows of Foreign Direct Investment are the most interesting of the FDI variables since
the theory is that inflows are what effect growth through technology advances in production,
as well as product introduction. It is the most available variable for FDI from the World Bank as
well.
Table 3: Correlation Matrix
FDI, net Gross Gross fixed School
GDP FDI*FSU Crude Log of
inflows (% of domestic capital enrollment,
Growth (1996- Birth Initial GDP Inflation
GDP) (1996- savings (% of formation (% of secondary (%
Rate 2000) Rate per capita
2000) GDP) GDP) gross)
FDI*FSU (1996-
0.375 1
2000)
FDI, net inflows
(% of GDP) 0.324 0.009 1
(1996-2000)
Gross domestic
savings (% of 0.104 -0.021 0.035 1
GDP)
Gross fixed
capital formation 0.026 -0.016 0.359 0.197 1
(% of GDP)
School
enrollment,
0.005 0.156 0.076 0.421 0.175 1
secondary (%
gross)
Crude Birth Rate -0.240 -0.143 -0.177 -0.097 0.027 -0.413 1
Log of Initial
-0.048 -0.072 0.162 0.549 0.219 0.796 -0.351 1
GDP per capita
Inflation 0.122 0.090 -0.009 0.001 -0.118 -0.159 -0.067 -0.220 1
Openness to
0.080 0.106 0.477 0.290 0.391 0.173 -0.228 0.250 0.048
Trade
Data from World Bank, World Development Indicators 2008 CD-Rom version.
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12. Table three presents the correlation matrix for the variables. There was a notable
correlation (0.477) between FDI inflows and openness to trade, which shows that it can be
beneficial to increase openness to trade to invite more FDI inflows. Another notable
correlation is that which is found between Secondary School Enrollment and the log of Initial
GDP per capita (0.796). This follows the theory of human capital supporting growth. Gross
Domestic Savings is also highly correlated (0.549) with the log of Initial GDP per capita.
Consequently, there is a correlation (0.421) between secondary school enrollment and savings.
It may be a result of better educated people making more money and thereby being able to
save more.
I use per capita GDP since family and individual welfare is at the heart of national
success. The dependent variables of Foreign Direct Investment, Gross Capital Formation, and
Gross Domestic Savings are included as percentages of GDP. This is to keep them relative to
the country’s economy size. This will show if they change relative to GDP. If it is shown that
they fall relative to GDP, especially if GDP is increasing in the meantime, shows that they are
less important to growth or possibly have a negative effect on growth. The opposite may also
be assumed.
V. Results
The model tested herein was run with the data from the World Bank and returned the
results found in Table 4. The coefficients that tested to be significant to the 1%, 5%, and 10%
levels are marked with three, two, or one stars respectively. The R-squared values, as well as
the f-statistics with degrees of freedom are also listed for each regression.
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13. Table 4: Regression Results with 1999 Base Year
Variable and Abbreviation 1 2 3 4 5 6 7
1.177253696*** 1.020616*** 0.5989597*** 1.02424*** 0.498204 0.3875706
FDI*FSU (1996-2000)
(0.000) (0.000) (0.000) (0.000) (0.180) (0.361)
FDI, net inflows (% of GDP) 0.099230* 0.066652 0.089695* 0.1688492*** 0.110500** 0.0586109 0.0897582*
(1996-2000) (0.071) (0.216) (0.082) (0.005) (0.043) (0.239) (0.068)
3.329632** 3.280747 4.851288***
FSU indicator
(0.035) (0.111) (0.000)
Gross domestic savings (% 0.0292815 0.0650209** 0.0316734 0.024789 0.0526232***
of GDP)
(0.019) (0.049) (0.270) (0.198) (0.009)
-
Crude Birth Rate 0.2380626***
(0.000)
Gross fixed capital 0.035555 0.0846482** 0.1477094*** 0.035264 0.0747151** 0.0555807
formation (% of GDP) (0.290) (0.019) (0.001) (0.289) (0.030) (0.102)
School enrollment, 0.0224695* 0.0130283 0.0153975 0.0125467
secondary (% gross) (0.056) (0.284) (0.203) (0.284)
-0.006216 -0.0062585 -0.0135856** -0.0146073** -0.007106
Openness to Trade
(0.322) (0.308) (0.046) (0.028) (0.254)
0.3644437
Inflation
(0.480)
-0.373711** -0.6998317*** -1.600056*** -0.7754999*** -0.317298* -0.543592** -0.7132263***
Log of Initial GDP per capita
(0.021) (0.006) (0.000) (0.006) (0.050) (0.040) (0.009)
R-squared 0.2468 0.3341 0.7162 0.4097 0.2688 0.3419 0.3729
F(6,152) F(6,125) F(6,54) F(8,112) F(7,151) F(6,126) F(6,125)
f-statistic
8.30 10.45 22.71 9.72 7.93 10.91 12.39
Data from World Bank, World Development Indicators 2008 CD-Rom version. Base year of 1999 unless otherwise specified.
(***), (**), and (*) indicate significance to the 1%, 5%, and 10% levels, respectively. Constant terms vary by regression and are
omitted.
The aim of this paper is to illustrate the effect of Foreign Direct Investment on growth
particularly in the former Soviet Union nations. As such there is an indicator FSU*FDI variable
for just those FSU nations to show the added benefit of FDI within those nations. When the
regression included FDI*FSU and FDI they were both significant to at least the 10% level. When
the indicator for FSU was also included, however, it lowered the significance of FDI*FSU.
Regression one included FDI*FSU, FDI, Gross Domestic Savings (GDS), Gross Fixed
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14. Capital Formation (as a percent of GDP) [GFCF], Openness to Trade, and the log of initial GDP
per capita. FDI*FSU was significant to the 1%, FDI was significant to the 10% level, and the log
of GDP per capita was significant to the 5% level.
The second regression excluded GDS while including Secondary School Enrollment which
was significant to the 10% level. FDI fell in significance to below the 10% level and fell in value a
little bit, while GFCF grew to the 5% level and more than doubled in value, and the log of GDP
per capita grew in significance to the 1% level and doubled in value.
The third regression included Crude Birth Rate (CBR) which is not available for many of
the countries, so it cut the regression down, but as a result it greatly increased the R-squared.
The fourth regression then omits CBR again, and changes some other control variables with
minor changes in the results, although the R-squared is better than the first two regressions.
The first four regressions all found FDI*FSU to be quite significant, while the significance
of FDI over all had varying significance. Clearly there was something different about the FSU
that was being captured in the interaction term. To determine whether it was in fact FDI, I
added the indicator FSU variable in the last three regressions. It was quite large and significant
while it reduced the significance of FDI*FSU.
The fifth and sixth regressions added in an indicator variable for FSU nations. The
addition of that variable removed significance of the FDI*FSU variable with little other changes
in the results. The seventh regression excluded FDI*FSU which increased the value and
significance of the FSU indicator variable as well as GDS significance. This lends some evidence
to there being a different more influential factor. There is certainly a difference between the
FSU and other nations, evidenced by the FSU indicator.
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15. It is important to note that in the seven regressions, there are some countries of the FSU
that are excluded because of missing information. Secondary School Enrollment data was not
available for the Russian Federation and Uzbekistan. Crude Birth Rates were not available for
Georgia, Moldova, and Tajikistan. Inflation was not available for Tajikistan and Uzbekistan.
Rule of Law (as well as the other 4 governance indicators) did not have data for Kyrgyz Republic
and the Russian Federation.
The interaction term of FDI*FSU was found to be significant in the first four regressions.
The values in these regressions range from 0.59 and 1.02, which shows that up to a whole
percent of the growth of GDP within FSU nations, can be account for by increasing FDI inflows
by one percent of GDP holding all else constant. It is significant to the 1% level in all six
regressions. That is quite significant considering that average growth over all nations in the
sample is about 2.5%.
The FSU indicator variable had coefficients 3.33, 3.28, and 4.85 in regressions 5, 6, and
7. It was also significant to the 5% level in regression 2, and to the 1% level in regression 7
which excluded the interaction term of FDI*FSU. This means that holding all else constant, just
the fact that a country was a member of the Soviet Union means that they are presently
growing up to almost 5% faster than average.
When the interaction term is included, it accounts for up to a whole percent of growth
above and beyond non FSU nations’ growth from FDI holding all else constant. When the
interaction term is included along with FDI and FSU separately, at least one loses significance.
The most robust of the three seems to be FDI*FSU, but since it is not significant along with FSU,
it cannot be as easily interpreted as robust and a real contributor to growth.
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16. As mentioned previously, the gains from FDI may be difficult to capture in a numerical
model. Convergence may even be supported more by FDI and trade than international
progress and it is easily argued that all work together for the progress of all. We have seen
many nations increase their growth and production upon opening borders to trade and learning
from imported products and foreign investors’ new ways of efficiency and production. It is
difficult to put numbers on these influences, but noticing a correlation between trade openness
and FDI flows with the growth of a nation, give evidence of such benefits.
Once again, however, this model fails to show that especially with the openness to trade
variable giving all negative coefficients and half of those calculated were significant to the 5%
level. What this shows is that according to my model, the more open a country is to trade,
holding all else constant, the less their economy will grow, in fact it will even grow slower as a
result of openness to trade up to 1.3% slower.
These results lend a good deal of evidence to convergence theory. The coefficient of
the natural log of initial GDP per capita was always negative and always significant to at least
the 10% level. In four of the seven regressions, it was significant to the 1% level, and was
lowest in the third regression at -1.6%. So, holding all else constant, the more GDP per capita a
nation had at the start, in the year 1999, the slower they grew on average.
Gross Fixed Capital Formation was also very significant in the analysis, and was well
correlated (0.359) to FDI inflows. This supports the long-term nature of FDI in developing
capital and structure which can raise productivity in the economy as a whole as well as in the
firms directly receiving FDI (Barrell and Holland 2000). Barrell and Holland also discovered that
when research is taken into account, FDI no longer has an impact on the economy, but the
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17. research does. When the research is brought to that country as a result of MNC’s production in
that country, then I believe it is still a result of FDI although maybe not as directly as the
regressions show.
There are some other variables in the model that are strongly significant in improving
growth. One such is the Crude Birth Rate, which was only available for 67 countries, but 9 of
the 12 FSU nations had that variable, and including it in the regression made the R-squared as
high as 0.71 for a greatly supported model. The coefficient for the birth rate was negative,
which follows the theory of population growth slowing as development occurs. That is a great
increase over the model with education, and not the birth rate, as the human development
indicator had R-squared value up to 0.41. This is still a reasonable model for such a large group
of such diversity since they include over a hundred nations with varying levels of development.
Inflation which would affect price levels and influencing trade did not test to be very
significant in the model. It was included to help determine robustness and to check and see if it
is significant. It actually tested to have a positive coefficient of 0.36 in regression four. It would
seem that inflation should have a negative impact on growth as higher prices would lessen
exports in theory. Openness to trade was included in that regression and it did have a negative
coefficient of -0.015 and was significant to the 5% level.
The interaction term FDI*FSU is insignificant when the FSU indicator variable is included
in the regression. This leads me to believe that there is a greater distinction between the FSU
and other nations which may be captured in governance indicators. It may be that countries
with better institutions and less corruption are able to better utilize FDI. In order to determine
the effect of governance and see if it would make a difference in the significance of the FDI*FSU
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18. (or any other) variables I found some governance indicators that could be included in the
regression. The regressions and analysis thereof follow.
Table 5: Governance Indicators
Variable and Abbreviation 1 2 3 4
0.2808042 0.2890947 0.2808042 0.2921956
FDI*FSU
(0.604) (0.506) (0.604) (0.503)
0.0655467 0.0658427 0.0655467 0.0655314
FDI, net inflows (% of GDP) (1996-2000)
(0.209) (0.208) (0.209) (0.209)
4.200222 4.151858* 4.200222 4.125981*
FSU indicator
(0.165) (0.057) (0.165) (0.063)
0.0617689* 0.0613066* 0.0617689* 0.061822*
Gross fixed capital formation (% of GDP)
(0.085) (0.094) (0.085) (0.085)
0.0173286 0.0171263 0.0173286 0.0172705
School enrollment, secondary (% gross)
(0.177) (0.184) (0.177) (0.178)
0.0320419
Rule of Law
(0.944)
0.0535104
Rule of Law *FSU
(0.972)
0.0003421
Rule of Law *FDI*FSU
(0.999)
-0.6212972** -0.6329134* -0.6212972** -0.6197108**
Log of Initial GDP per capita
(0.028) (0.060) (0.028) (0.028)
R-squared 0.3715 0.3715 0.3715 0.3715
F(7,115) F(7,115) F(7,115) F(7,115)
f-statistic
9.71 9.71 9.71 9.71
Data from World Bank, World Development Indicators 2008 CD-Rom version. Base year of 1999 unless otherwise specified.
(***), (**), and (*) indicate significance to the 1%, 5%, and 10% levels, respectively. Constant terms vary by regression and are
omitted. This model is the same as the 6th regression in the first model plus the governance variables. Regulatory Quality
provided by the World Bank website from "Governance Matters VIII: Governance Indicators for 1996-2008" by Kaufmann et al.
Breusch-Pagan test on regression 2 resulted in a chi-squared of 0.24 with probability > chi-squared of 0.6262.
The results with the governance indicator of Rule of Law are not significantly different
from the previous model and its regressions. I used the model from the sixth regression in the
first set adding in the governance variable. One difference is that the FSU indicator variable
went up by almost a whole percent from 3.28 to 4.2. The governance variable, however, never
tested to be significant.
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19. VI. Conclusion:
Growth of the former Soviet Union nations was analyzed in this paper in an effort to
identify the benefit they receive from foreign direct investment. An ordinary least squares
model was used to calculate growth benefits of the nations from factors commonly accepted to
affect Gross Domestic Product.
In my analysis, I found that after the breakup of the Soviet Union there was a downturn
in economic growth per capita, but within five years of the breakup most countries were
recovered and resuming growth. Because of this growth pattern, I ran some regressions to see
if FDI might have been a driving influence in the recovery of the nations that lost GDP per capita
for a few years. As such an interaction term of FDI*FSU was included in the analysis in an effort
to determine if FDI was a greater factor in the growth of FSU nations than other nations and it
was found to be significant until the FSU indicator variable was added. FDI*FSU was expected
to have more influence in the FSU than other nations because their technology and socialistic
market structures were lagging capitalist industrialized nations. However, the indicator variable
absorbed the significance, and had a much larger value than FDI*FSU. This may be because of
the difficulty of capturing all the benefits of FDI in numerical data, or it could be a result of
omitted variables.
Since the Soviet Union has only been broken up since 1991 and data collection has not
been expansive, so the results found here may later be verified or disproved as more reliable
data becomes available and especially as the more far reaching effects of the break up are
magnified with time. From the structure of my model and the numerical analysis, it is difficult
to say with much surety that there is a positive correlation between FDI inflows as a percent of
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20. GDP and GDP growth rates. The more time goes on, and the more transparent the transitioning
economies become, the more reliable these analyses can be. It seems at this time, however,
that FDI should be encouraged to assist these nations especially those which have financial
structures and human capital to more effectively utilize and benefit from such.
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